Ensemble empirical mode decomposition and a long short-term memory neural network for surface water quality prediction of the Xiaofu River, China

Abstract

Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD-LSTM was proposed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD-LSTM for NH3N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the proposed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD-LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.

Salinity prediction in Qiantang Estuary based on Improved LSTM model

“…Compared to numerical models, these models can establish relationships between parameters and avoid the constraints imposed by complex boundaries or initial conditions 14 . Estuarine salinity values constitute a typical nonlinear time series, and conventional Artificial Neural Network (ANN) models fail to capture the sequential information during training, leading to suboptimal performance in predicting nonlinear salinity 15 . Therefore, scholars proposed the Long Short-Term Memory (LSTM) model based on the Recurrent Neural Network (RNN) 16 .…”